AI-Driven Solutions for Efficient Detection of Banking Fraud

Abstract

The financial sector has been increasingly susceptible to fraudulent activities due to its extensive use of digital platforms, increasing transaction volumes, and evolving fraud techniques. Traditional methods for detecting fraud are becoming inadequate, leading to significant financial losses. Artificial Intelligence (AI) offers innovative, efficient, and scalable solutions to combat this growing threat. This research paper explores AI-driven approaches for the detection of banking fraud, focusing on the implementation of machine learning algorithms, neural networks, and real-time data analytics. Key challenges, such as the dynamic nature of fraud, handling large datasets, and ensuring accuracy in predictions, are also discussed. The paper concludes with a review of AI's current limitations and future potential in creating robust fraud detection frameworks.